Text-to-Image
Diffusers
StableDiffusionPipeline
lmd
llm-grounded-diffusion
lmd-plus
layout-to-image
text-to-layout
text-to-layout-to-image
llm
stable-diffusion
stable-diffusion-diffusers
Instructions to use longlian/lmd_plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use longlian/lmd_plus with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("longlian/lmd_plus", dtype=torch.bfloat16, device_map="cuda") prompt = "In an indoor scene, a blue cube directly above a red cube with a vase on the left of them" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_diffusers_version": "0.2.2", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPFeatureExtractor" | |
| ], | |
| "safety_checker": [ | |
| "stable_diffusion", | |
| "StableDiffusionSafetyChecker" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "PNDMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |